I am the founder of Aryng, a management consulting firm focused on analytics developing our client's own internal capability - people, process and tool, to leverage data for making smarter decisions. I work with individuals who are looking to transition their career to analytics or those who are looking to leverage analytics in their work. On the corporate side, I work with CEO/GM, CMO and Chief Product Officer to develop their teams by consulting, training, coaching, recruiting, setting up processes and the right tools.
I have 15 years of experience in analytics with $150M+ demonstrated impact on business at Fortune 500 companies like PayPal and Adobe. Additionally, I keynote at analytics and business conferences sharing my thoughts on how organizations can be transformed through the power of analytics. I am passionate about empowering professionals with tools and framework to enable smarter decisions using data.
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To Centralize Analytics or Not, That is the Question

This is a contributed article by my colleague, Mukul Patki. Mukul has 10+ years of experience in Analytics and is Aryng‘s main instructor on Experimentation. I asked Mukul to share some words of wisdom addressing burning issues around organization of analytics, here is his first post on the common Centralized vs. Distributed Analytics Group dilemma!

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The structure of analytics in large organizations can take many forms—from having a gazillion analytics micro-teams embedded in each function or BU, to completely centralized analytics at the corporate level. What is the right strategy? What should your organization do?

Well, in that respect, the title of this post is misleading. To centralize or not to centralize, is actually NOT the question. If you think of centralization on a scale going from ‘not at all’ to ‘fully centralized’, the real question is what is the right level for you?

To answer that question you must be aware of the pros and cons of moving one way or the other on that scale. Having been a part of multiple “re-orgs” and that have gone up and down on the scale, and having influenced some of those movements some of the time, I have some first hand insight into this.

So here are the top 5 key trade-offs when faced with organizational structure of analytics.

1. Consultant Mindset vs. Deep Personal Investment: God bless consultants, they often save the day! But one thing they cannot claim is deep emotional investment in the organization they are working for. This is what high degree of centralization does. Analysts are assigned to BU’s or functions based on prioritization of the project and resource constraints. Their mindset is like that of a consultant, where you work on a project, crunch the numbers, deliver the insights and you job is done… time to move on to the next one. With analytics embedded within the function, there can be full integration of analytics with the project right from its conception. The alignment of purpose this creates, produces very non-linear synergistic effects with respect to the value derived from analytics. This alignment/ownership, of course could be a problem by itself, which brings us to the next point

2. Objectivity (or at least the perception of it): If the analytics team reports into the owner of the domain, and their rewards are aligned with the success of the projects being analyzed, the objectivity of the analysis could be in question. The analyst could potentially introduce a bias to make the project/initiative look better than it actually is. With analytics, credibility is everything. The perception of lack of objectivity could be devastating for the entire group/organization. If you believe that numbers cannot lie, you are either not in the field of analytics or are deluded. Read How To Lie With Statistics for starters.

3. Bureaucracy vs. Efficiency: Centralization brings bureaucracy; sometimes copious amounts of bureaucracy, depending on who is the heading analytics. Everything needs to get into the pipeline, and get prioritized, and get resources allocated against it. There are protocols for communication, to ensure the Business Units are not side stepping the process (this seems like paranoia but I have experienced this first hand). It could suck the excitement out of a very creative job (I am talking about analytics of course), and turn analysts into full time project managers (God bless project managers, I have nothing against them either).

4. Redundancy vs. Effectiveness: With the “embedded” model, it is easy for different analytics teams to get redundant in their analyses and continually reinvent the proverbial wheel. Centralization dramatically reduces redundancy, thus making the analytics team more effective. There is more knowledge sharing, a better sense of community of like-minded people, and more flexibility in leveraging a wide range of skill sets among analysts. This improves the throughput by improving the utilization of resources, thus also making the team lean.

5. Silos vs. Big Picture: Small teams of analysts embedded within the BU end up working in silos. While they become experts in their own domain, they run the risk of losing the big picture. This can be detrimental not only to the quality and relevance of the insights generated, but also to the career growth prospects and job satisfaction of the members of analytics team.

So that brings us the decision point—what is the right level of centralization. Business Units or functional teams will always resist centralization of analytics because they would not get dedicated capacity anymore. Analysts, on the other hand, would likely (but not always) resist decentralization. The holy grail is to find the level at which both the stakeholders are equally happy (or equally unhappy!), such that analysts get some opportunity to move around, cross-train and gain breadth of domain, and at the same time, have the chance to develop deep domain knowledge in a specific part of the organization and to influence/drive the strategy for the Business Unit as opposed to reporting out data. Finding that sweet spot is not easy, but this hopefully gives you a sense of what you are looking for in the first place.

For more details about how we approach analytics and how analytics can drive business impact, download analytics whitepapers from here. And if we can help your organization in the journey towards being data-driven, feel free to contact us.

If you are an executive, tired of making decisions without facts – which often is the best you can do, but perhaps keeps you up at night, then here is the good news. It is possible to transform your organization that you do not have to fly blind anymore. The transformation begins with this ½ day Data-Driven Executive Workshop on April 5th, 2013, in Santa Clara, CACA.

If you are a marketer, product manager, operations professional, ready to equip yourself with the “Data to Decisions” framework, start by taking our “Data to Decisions” intro analytics course online today! Once you have taken the level-1 course, you would get access to level-2, “Hands on Analytics” course and Level-3, “Hands-on A/B Testing” course online to complete your “Data to Decisions” skill upgrade.

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